No abstract

Urban e-mobility, seen as a part of complex and multidimensional European Green Deal plan, is essential for cities. However, it cannot be implemented without a common social commitment accompanied by a shared, strong belief in its advantages. Even if urban authorities and central governments would encourage their citizens to buy or share an electric vehicle (EV), the shift to EV will not be significant without people convinced that the idea of becoming zero-emission is economically viable and rational to them privately. This is especially true and important in countries like Poland—which is classified as an “EV readiness straggler”. The main purpose of this study is to develop a robust forecasting model with the aid of advanced machine learning methods. Based on the survey conducted, we identified factors useful for predicting consumer behaviour in terms of willingness to purchase an EV. The proposed machine-learning tool (specifically, the Random Forest algorithm) will allow automotive companies to more effectively target factors supporting the promulgation of urban individual e-mobility.

The preparation stage of PPPs is crucial in order to avoid unsuccessful negotiations that lead to sunk costs.  The key to successful negotiations is to construct an effective contract reconciling the differing interests of all parties. Therefore, identification of the most important features of the project from both partners’ points of view is extremely important to ultimately conclude the negotiations successfully. The purpose of the study is to test and assess the usefulness of an innovatively adapted conjoint analysis method aimed at identifying the preferences of both partners involved in PPP.

No abstract

PPP‐style cooperation between public and private partners is becoming more popular in Poland. Nevertheless, the issue of intergenerational responsibility for its effects is not taken into consideration while assessing the outcomes and drawbacks of PPP contracts. The author examines several Polish PPP undertakings in order to delineate the most important components of intergenerational responsibility.